Empowering Users with Narratives: Examining the Efficacy of Narratives for Understanding Data-Oriented Conceptual Models

Merete Hvalshagen (), Roman Lukyanenko () and Binny M. Samuel ()
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Merete Hvalshagen: University of Dayton, Dayton, Ohio 45469
Roman Lukyanenko: University of Virginia, Charlottesville, Virginia 22903
Binny M. Samuel: University of Cincinnati, Cincinnati, Ohio 45221

Information Systems Research, 2023, vol. 34, issue 3, 890-909

Abstract: With the ongoing digitalization of human society, regular employees (e.g., noninformation technology experts) become increasingly autonomous and proactive in using organizational data and information technologies to facilitate data-driven actions and insights. This growing autonomy of regular employees (dubbed here, empowered users ) in using information technologies creates new challenges. As most empowered users lack sophisticated information technology skills, they struggle to find and access relevant data, understand their meaning, and extract and adapt them to meet their needs. We propose a powerful way to support empowered users with a combination of conceptual models and narratives. A data narrative is a descriptive and textual representation of one or more aspects of data in a domain that is organized and presented as a connected sequence of sentences in a natural language. We assess the efficacy of narrative representations for conceptual modeling cardinality constraints, which are an essential part of understanding data. We conducted two laboratory experiments and found a positive and strong effect for narratives: cardinality constraints that were described in the narratives were better understood than constraints only represented in the model (i.e., script). This effect was robust across cardinalities, measures, script complexities, and familiarity levels with the conceptual modeling grammar.

Keywords: empowered users; conceptual modeling; data management; data analytics; narratives; laboratory experiment (search for similar items in EconPapers)
Date: 2023
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